Study of Engineered Features and Learning Features in Machine Learning - A Case Study in Document Classification
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- 关键词：Deep learning ; Feature extraction ; Autoencoder ; Restricted Boltzmann Machine ; Semantic association ; N ; gram Model
- 刊名：Lecture Notes in Computer Science
- 出版年：2017
- 出版时间：2017
- 年：2017
- 卷：10127
- 期：1
- 页码：161-172
- 丛书名：Intelligent Human Computer Interaction
- ISBN：978-3-319-52503-7
- 卷排序：10127
文摘
Document classification is challenging due to handling of voluminous and highly non-linear data, generated exponentially in the era of digitization. Proper representation of documents increases efficiency and performance of classification, ultimate goal of retrieving information from large corpus. Deep neural network models learn features for document classification unlike the engineered feature based approaches where features are extracted or selected from the data. In the paper we investigate performance of different classifiers based on the features obtained using two approaches. We apply deep autoencoder for learning features while engineering features are extracted by exploiting semantic association within the terms of the documents. Experimentally it has been observed that learning feature based classification always perform better than the proposed engineering feature based classifiers.